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Predictors of psychiatric rehospitalization among elderly patients.

ABSTRACT: The population of Hong Kong and the proportion of elderly people have been increasing rapidly. The aim of this retrospective cohort study is to determine predictive factors for psychiatric rehospitalization within 2 years among elderly patients who were discharged from psychiatric wards, in attempt to reduce their rehospitalization rate and to reintegrate them into the community. Patients aged 65 and over, who were discharged from psychiatric wards of Pamela Youde Nethersole Eastern Hospital from 1 March 2010 to 29 February 2012, were identified. Rehospitalization within 2 years after discharge was the primary outcome measure, and the time to rehospitalization was measured as the secondary outcome. Patients were subgrouped into readmitted and non-readmitted groups. Logistic regression and Cox regression analyses were applied to the potential predictive factors with odds ratios and hazard ratios obtained, respectively, for the significant findings. Kaplan-Meier survival curves were plotted for graphical representation of the study results in survival analysis. 368 individuals satisfying the study criteria were identified. The same four factors were shown to be significantly associated with rehospitalization in both multiple logistic regression and Cox regression survival analysis. Referral to other psychiatric disciplines upon discharge (p< 0.001, OR=0.325, HR=0.405) was associated with a lower rehospitalization risk and correlated to a longer time to rehospitalization. History of suicidal behaviors (p< 0.001, OR=4.906, HR=3.161), history of violent behaviors (p< 0.001, OR=5.443, HR=3.935) and greater number of previous psychiatric admissions (p< 0.001, OR=1.250, HR=1.121) were associated with a higher rehospitalization risk and predicted earlier rehospitalization. The rehospitalization rate of elderly patients was 5.2% at 1 month, 9.5% at 3 months, 15.0% at 6 months, 17.1% at 1 year, 18.8% at 1.5 year and 20.9% at 2 years.

SUBMITTER: Wong CY

PROVIDER: S-EPMC4732553 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Predictors of psychiatric rehospitalization among elderly patients.

Wong Chun Yin Terry CY

F1000Research 20150930

The population of Hong Kong and the proportion of elderly people have been increasing rapidly. The aim of this retrospective cohort study is to determine predictive factors for psychiatric rehospitalization within 2 years among elderly patients who were discharged from psychiatric wards, in attempt to reduce their rehospitalization rate and to reintegrate them into the community. Patients aged 65 and over, who were discharged from psychiatric wards of Pamela Youde Nethersole Eastern Hospital fro ...[more]

PMID: 26870319

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Project description:BackgroundHeart failure (HF) is associated with a high rate of readmissions within 30 days post-discharge and in the following year, especially in frail elderly patients. Biomarker data are scarce in this high-risk population. This study assessed the value of early post-discharge circulating levels of ST2, NT-proBNP, CA125, and hs-TnI for predicting 30-day and 1-year outcomes in comorbid frail elderly patients with HF with mainly preserved ejection fraction (HFpEF).MethodsBlood samples were obtained at the first visit shortly after discharge (4.9 ± 2 days). The primary endpoint was the composite of all-cause mortality or HF-related rehospitalization at 30 days and at 1 year. All-cause mortality alone at one year was also a major endpoint. HF-related rehospitalizations alone were secondary end-points.ResultsFrom February 2014 to November 2016, 522 consecutive patients attending the STOP-HF Clinic were included (57.1% women, age 82 ± 8.7 years, mean Barthel index 70 ± 25, mean Charlson comorbidity index 5.6 ± 2.2). The composite endpoint occurred in 8.6% patients at 30 days and in 38.5% at 1 year. In multivariable analysis, ST2 [hazard ratio (HR) 1.53; 95% CI 1.19-1.97; p = 0.001] was the only predictive biomarker at 30 days; at 1 year, both ST2 (HR 1.34; 95% CI 1.15-1.56; p < 0.001) and NT-proBNP (HR 1.19; 95% CI 1.02-1.40; p = 0.03) remained significant. The addition of ST2 and NT-proBNP into a clinical predictive model increased the AUC from 0.70 to 0.75 at 30 days (p = 0.02) and from 0.71 to 0.74 at 1 year (p < 0.05). For all-cause death at 1 year, ST2 (HR 1.50; 95% CI 1.26-1.80; p < 0.001), and CA125 (HR 1.41; 95% CI 1.21-1.63; p < 0.001) remained independent predictors in multivariable analysis. The addition of ST2 and CA125 into a clinical predictive model increased the AUC from 0.74 to 0.78 (p = 0.03). For HF-related hospitalizations, ST2 was the only predictive biomarker in multivariable analyses, both at 30 days and at 1 year.ConclusionsIn a comorbid frail elderly population with HFpEF, ST2 outperformed NT-proBNP for predicting the risk of all-cause mortality or HF-related rehospitalization. ST2, a surrogate marker of inflammation and fibrosis, may be a better predictive marker in high-risk HFpEF.

Project description:BackgroundChronic heart failure (CHF) is a severe condition, often co-occurring with depression and anxiety, that strongly affects the quality of life (QoL) in some patients. Conversely, depressive and anxiety symptoms are associated with a 2-3 fold increase in mortality risk and were shown to act independently of typical risk factors in CHF progression. The aim of this study was to examine the impact of depression, anxiety, and QoL on the occurrence of rehospitalization within one year after discharge in CHF patients.Methods148 CHF patients were enrolled in a 10-center, prospective, observational study. All patients completed two questionnaires, the Hospital Anxiety and Depression Scale (HADS) and the Questionnaire Short Form Health Survey 36 (SF-36) at discharge timepoint.ResultsIt was found that demographic and clinical characteristics are not associated with rehospitalization. Still, the levels of depression correlated with gender (p ≤ 0.027) and marital status (p ≤ 0.001), while the anxiety values were dependent on the occurrence of chronic obstructive pulmonary disease (COPD). However, levels of depression (HADS-Depression) and anxiety (HADS-Anxiety) did not correlate with the risk of rehospitalization. Univariate logistic regression analysis results showed that rehospitalized patients had significantly lower levels of Bodily pain (BP, p = 0.014), Vitality (VT, p = 0.005), Social Functioning (SF, p = 0.007), and General Health (GH, p = 0.002). In the multivariate model, poor GH (OR 0.966, p = 0.005) remained a significant risk factor for rehospitalization, and poor General Health is singled out as the most reliable prognostic parameter for rehospitalization (AUC = 0.665, P = 0.002).ConclusionTaken together, our results suggest that QoL assessment complements clinical prognostic markers to identify CHF patients at high risk for adverse events.Clinical trial registrationThe study is registered under http://clinicaltrials.gov (NCT01501981, first posted on 30/12/2011), sponsored by Charité - Universitätsmedizin Berlin.

Project description:ImportanceThe effectiveness of antipsychotic polypharmacy in schizophrenia relapse prevention is controversial, and use of multiple agents is generally believed to impair physical well-being.ObjectiveTo study the association of specific antipsychotic combinations with psychiatric rehospitalization.Design, setting, and participantsIn this nationwide cohort study, the risk of psychiatric rehospitalization was used as a marker for relapse among 62 250 patients with schizophrenia during the use of 29 different antipsychotic monotherapy and polypharmacy types between January 1, 1996, and December 31, 2015, in a comprehensive, nationwide cohort in Finland. We conducted analysis of the data from April 24 to June 15, 2018. Rehospitalization risks were investigated by using within-individual analyses to minimize selection bias.Main outcomes and measuresHazard ratio (HR) for psychiatric rehospitalization during use of polypharmacy vs during monotherapy within the same individual.ResultsIn the total cohort, including 62 250 patients, 31 257 individuals (50.2%) were men, and the median age was 45.6 (interquartile range, 34.6-57.9) years. The clozapine plus aripiprazole combination was associated with the lowest risk of psychiatric rehospitalization in the total cohort, being superior to clozapine, the monotherapy associated with the best outcomes, with a difference of 14% (HR, 0.86; 95% CI, 0.79-0.94) in the analysis including all polypharmacy periods, and 18% in the conservatively defined polypharmacy analysis excluding periods shorter than 90 days (HR, 0.82; 95% CI, 0.75-0.89; P < .001). Among patients with their first episode of schizophrenia, these differences between clozapine plus aripiprazole vs clozapine monotherapy were greater (difference, 22%; HR, 0.78; 95% CI, 0.63-0.96 in the analysis including all polypharmacy periods, and difference, 23%; HR, 0.77; 95% CI, 0.63-0.95 in the conservatively defined polypharmacy analysis). At the aggregate level, any antipsychotic polypharmacy was associated with a 7% to 13% lower risk of psychiatric rehospitalization compared with any monotherapy (ranging from HR, 0.87; 95% CI, 0.85-0.88, to HR, 0.93; 95% CI, 0.91-0.95; P < .001). Clozapine was the only monotherapy among the 10 best treatments. Results on all-cause and somatic hospitalization, mortality, and other sensitivity analyses were in line with the primary outcomes.Conclusions and relevanceCombining aripiprazole with clozapine was associated with the lowest risk of rehospitalization, indicating that certain types of polypharmacy may be feasible in the treatment of schizophrenia. Because add-on treatments are started when monotherapy is no longer sufficient to control for worsening of symptoms, it is likely that the effect sizes for polypharmacy are underestimates. Although the results do not indicate that all types of polypharmacy are beneficial, the current treatment guidelines should modify their categorical recommendations discouraging all antipsychotic polypharmacy in the maintenance treatment of schizophrenia.

Project description:BackgroundAccurately estimating elderly patients' rehospitalisation risk benefits clinical decisions and service planning. However, research in rehospitalisation and repeated hospitalisation yielded only models with modest performance, and the model performance deteriorates rapidly as the prediction timeframe expands beyond 28 days and for older participants.MethodsA temporal zero-inflated Poisson (tZIP) regression model was developed and validated retrospectively and prospectively. The data of the electronic health records (EHRs) contain cohorts (aged 60+) in a major public hospital in Hong Kong. Two temporal offset functions accounted for the associations between exposure time and parameters corresponding to the zero-inflated logistic component and the Poisson distribution's expected count. tZIP was externally validated with a retrospective cohort's rehospitalisation events up to 12 months after the discharge date. Subsequently, tZIP was validated prospectively after piloting its implementation at the study hospital. Patients discharged within the pilot period were tagged, and the proposed model's prediction of their rehospitalisation was verified monthly. Using a hybrid machine learning (ML) approach, the tZIP-based risk estimator's marginal effect on 28-day rehospitalisation was further validated, competing with other factors representing different post-acute and clinical statuses.ResultsThe tZIP prediction of rehospitalisation from 28 days to 365 days was achieved at above 80% discrimination accuracy retrospectively and prospectively in two out-of-sample cohorts. With a large margin, it outperformed the Cox proportional and linear models built with the same predictors. The hybrid ML revealed that the risk estimator's contribution to 28-day rehospitalisation outweighed other features relevant to service utilisation and clinical status.ConclusionsA novel rehospitalisation risk model was introduced, and its risk estimators, whose importance outweighed all other factors of diverse post-acute care and clinical conditions, were derived. The proposed approach relies on four easily accessible variables easily extracted from EHR. Thus, clinicians could visualise patients' rehospitalisation risk from 28 days to 365 days after discharge and screen high-risk older patients for follow-up care at the proper time.

Project description:IntroductionElderly patients are at increased risk of developing sepsis and its adverse outcomes. Diagnosing and prognosing sepsis is particularly challenging in older patients, especially early at emergency department (ED) arrival. We aimed to study and compare the characteristics of elderly and very elderly ED patients with sepsis and determine baseline factors associated with in-hospital mortality. We also compared prognostic accuracy of the criteria for systemic inflammatory response syndrome, quick sequential organ failure assessment (qSOFA), and the National Early Warning Score in predicting mortality.MethodsWe conducted a retrospective study at the ED of Siriraj Hospital Mahidol University in Bangkok, Thailand. Patients over 18 years old who were diagnosed and treated for sepsis in the ED between August 2018-July 2019 were included. We categorized patients into non-elderly (aged <65 years), elderly (aged 65-79 years), and the very elderly (aged >80 years) groups. The primary outcome was in-hospital mortality. Baseline demographics, comorbidities, source and etiology of sepsis, including physiologic variables, were compared and analyzed to identify predictors of mortality. We calculated and compared the area under the receiver operator characteristics curves (AUROC) of early warning scores.ResultsOf 1616 ED patients with sepsis, 668 (41.3%) were very elderly, 512 (31.7%) were elderly, and 436 (27.0%) were non-elderly. The mortality rate was highest in the very elderly, followed by the elderly and the non-elderly groups (32.3%, 25.8%, and 24.8%, respectively). Factors associated with mortality in the very elderly included the following: age; do-not-resuscitate (DNR) status; history of recent admission <3 months; respiratory tract infection; systolic blood pressure <100 millimeters mercury (SBP<100); oxygen saturation; and Glasgow Coma Scale (GCS) score. Factors associated with mortality in the elderly were DNR status, body temperature, and GCS score. qSOFA had the highest AUROC in predicting in-hospital mortality in both very elderly and elderly patients (AUROC 0.60 [95% confidence interval {CI}, 0.55-0.65] and 0.55 [95% CI, 0.49-0.61, respectively]).ConclusionThe mortality rate in the very elderly was higher than in the younger populations. Age, DNR status, recent admission, respiratory tract infection, SBP<100, oxygen saturation. and GCS score independently predicted hospital mortality in very elderly patients. The qSOFA score had better but only moderate accuracy in predicting mortality in elderly and very elderly sepsis patients.

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Predictors of psychiatric rehospitalization among elderly patients.

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Predictors of psychiatric rehospitalization among elderly patients.

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